Soil & Water Spatial Variability

• Spatial vs temporal variability: definitions and implications
  
• Soil texture: particle-size distribution affecting water-holding, nutrient retention, and root growth
  
• Soil structure: aggregation patterns influencing aeration, water infiltration, and seasonal moisture dynamics
  
• Mapping methods: electromagnetic induction (ECa) for texture proxies and gamma radiometrics for structural heterogeneity
  
• Seasonal effects: how moisture fluctuation alters conductivity and radiometric signatures
  
• Variability pattern analysis as basis for management zone delineation

Agenda

• Precision Agriculture & Site-Specific Nutrient Management Overview
  
• 4R Nutrient Stewardship Framework (Right Product, Rate, Place, Time)
  
• Grid Soil Sampling & Spatial Data Acquisition
  
• Geostatistical Interpolation Methods (Kriging, IDW, Cokriging)
  
• Prescription Map Generation & GNSS-Guided Variable-Rate Application
  
• Monitoring, PDCA Cycle & Feedback for Continuous Optimization

Learning Objectives

• Understand the functionality and architecture of the Smart-Map QGIS plugin
  
• Learn the principles of Ordinary Kriging (OK) and Support Vector Machines (SVM) for digital soil mapping
  
• Explore the use of Moran’s I for spatial covariate selection
  
• Review model evaluation metrics: R² and RMSE
  
• Compare predictive performance of OK vs SVM in soil chemical attribute mapping
  
• Identify data requirements, sampling density, and workflows for a 75 ha field study

Soil Formation & Landscape Variability

• Multi-scale soil and crop variability mapping: expert surveys, digital soil mapping, proximal/remote sensing, yield mapping
  
• Strategic, tactical, operational decision levels for low- and high-tech systems
  
• Management unit delineation based on pedogenic factors and landscape morphology
  
• ICT-driven spatial integration linking soil heterogeneity to crop growth and input scheduling
  
• Site-specific nutrient and water application to minimize leaching, runoff, and GHGs
  
• Economic optimization through precision targeting of inputs to variable-rate zones

Key Formation Processes

graph TD
  A[Climate] --> F(Soil Formation)
  B[Organisms] --> F
  C[Relief] --> F
  D[Parent Material] --> F
  E[Time] --> F

• Climate: chemical & physical weathering rates
  
• Organisms: organic matter inputs & bioturbation
  
• Relief: erosion, drainage, deposition
  
• Parent material: mineralogy, texture, pH
  
• Time: horizon differentiation & pedogenesis
  
• CLORPT factors as spatial covariates

Weathering & Horizon Development

graph TD
    A[Parent Material<br>Bedrock] --> B[Weathering<br>Physical, Chemical, Biological]
    B --> C[Additions<br>Organic matter]
    B --> D[Losses<br>Leaching]
    B --> E[Translocations<br>Clay, minerals]
    B --> F[Transformations<br>Humus formation]
    C --> G[Horizon Development]
    D --> G
    E --> G
    F --> G

Organic Matter Accumulation

• Organic matter enhances soil structure & CEC
  
• Humus content correlates with higher ECa
  
• Organic-rich zones map distinctly on ECa surveys
  
• Decomposition alters moisture retention & conductivity
  
• Overlaps with anthropogenic management zones
  
• ECa surveys delineate organic gradients for VRA

Landscape Controls on Variability

flowchart LR
  A[Soil Sampling] --> B[Lab Analysis]
  B --> C[Variability Map]
  C --> D[Zone Delineation]
  D --> E[VRA]

Soil Physical Properties Overview

• Apparent Conductivity (ECa): salt, clay, moisture, bulk density, OM, temperature
  
• EMI vs electrode arrays for measurement
  
• Spatial mapping: salinity, clay stratification, moisture, sand
  
• Anthropogenic effects: leaching, drainage, compaction
  
• ECa as proxy for water & nutrient zones
  
• Correlation with yield for site-specific management

flowchart LR
  A[Soil Props] --> B(ECa Signal)
  C[Env Factors] --> B
  B --> D[Variability Maps]
  D --> E[Zones]
  E --> F[Recommendations]

Soil Texture

graph LR
  EM[EMI] --> Data[ECa Data]
  RES[Resistivity] --> Data
  Data --> Map[Georeferenced Map]
  Map --> Analysis[Variability]
  Analysis --> Zones[Management Zones]

Soil Structure

graph TD
  A[Structure] --> B[Aggregate Classes]
  A --> C[Proximal Sensors]
  C --> D[Porosity Map]
  C --> E[Bulk Density]
  E --> F[Compaction Zones]
  F --> G[Root Constraints]
  G --> H[Yield Impacts]

Soil Porosity & Density

• Porosity: void fraction controlling aeration, water, roots
  
• Bulk density: mass per volume, inversely related to porosity
  
• Core & gamma-ray methods for direct measurement
  
• ECa sensitivity to density via dielectric coupling
  
• Heterogeneity drives mapping resolution
  
• Use for precision tillage & irrigation zoning

flowchart LR
  Porosity -->|inverse| BulkDensity
  BulkDensity --> SensorCoupling
  SensorCoupling --> ECa
  Porosity --> WaterRetention
  WaterRetention --> ECa

Spatial Variability of Physical Properties

• Texture heterogeneity impacts water & nutrients
  
• Aggregate stability & pore connectivity vary spatially
  
• Bulk density & compaction hotspots alter roots & aeration
  
• Infiltration & retention capacity create moisture gradients
  
• Topographic gradients drive erosion & deposition
  
• Geostatistical methods (variogram, kriging) define zones

Macropores & Micropores

Soil Compaction & Traffic Farming

• Controlled Traffic Farming confines wheels to lanes, preventing subsoil compaction (>0.3 MPa @ 400 mm)
  
• Preserves macroporosity & root penetration
  
• Eliminates deep tillage energy costs
  
• Improves infiltration, reduces runoff, erosion, GHGs
  
• GNSS-guided implements ensure precise lane alignment
  
• VRA within lanes optimizes input use

Soil Chemical Properties Overview

• ECa integrates soluble salts, clay mineralogy, moisture, bulk density, organic matter, temperature
  
• EMI & ERT surveys for rapid field-scale mapping
  
• Spatial ECa patterns reveal salinity, clay lenses, moisture trends
  
• Depth profiling differentiates shallow sand vs deep clay
  
• Organic matter & management practices modulate signals
  
• ECa as proxy for yield-limiting properties

Soil pH Variability

graph LR
  A[EMI Survey] --> B[ECa Map]
  B --> C[pH Model]
  C --> D[Lime VRA Map]
  D --> E[Yield Benefit]

Soil Nutrient Content

graph LR
  A[EMI Sensor] --> B[ECa Data]
  B --> C[GIS Interpolation]
  C --> D[ECa Map]
  D --> E[Zones]
  E --> F[VRA]

Cation Exchange Capacity

• CEC: capacity to retain/exchange Ca²⁺, Mg²⁺, K⁺, Na⁺
  
• Units: cmol(+)/kg or meq/100 g
  
• Governs nutrient buffering & leaching risk
  
• Spatial mapping via grid sampling + cokriging
  
• Informs 4R management: rate & placement
  
• Enhances fertilizer efficiency

Soil Nitrate & Water Relations

• Nitrate variability affects water-use efficiency & leaching risk
  
• Temporal nitrate dynamics under variable irrigation
  
• 7-R framework: adds form, feedback, resilience to 4Rs
  
• Geo-tools enable nitrate VRA & irrigation control
  
• Erosion controls reduce runoff
  
• Riparian buffers & wetlands attenuate drainage nitrates

Role of Soil Mapping

• Integrate surveys, digital mapping, remote sensing for zone delineation
  
• Deploy proximal sensors + interpolation for high-res layers
  
• Monitor moisture, nutrients, OM over time
  
• VRA fertilization, tillage, irrigation recommendations
  
• Strategic → tactical → operational decision hierarchy
  
• Long-term soil health & environmental monitoring

Interpreting Soil Variability

flowchart LR
  EC[EC Sensor] --> IN[Integration]
  GR[Gamma Radiometry] -->
  HT[Hand Texturing] --> IN
  IN --> MAP[Variability Map]
  MAP --> ZONES[Management Zones]

Soil Mapping Technologies

• Traditional expert surveys & farmer knowledge
  
• Digital Soil Mapping: geostatistics + machine learning
  
• In situ sensor networks: EC, moisture, pH, nutrients
  
• Remote sensing: multispectral/hyperspectral, UAV
  
• Crop sensing (NDVI, yield) for soil–crop dynamics
  
• Integrated GIS management units

Creating a Basic Soil Map

graph LR
  A[Sampling Grid] --> B[GPS Georeferencing]
  B --> C[Variogram Analysis]
  C --> D[Spatial Interpolation]
  D --> E[Digital Map]
  E --> F[Validation]

• Design grid by field size & zones
  
• Georeference samples with high-precision GPS
  
• Model variogram for spatial autocorrelation
  
• Use kriging or IDW for surfaces
  
• Integrate ancillary data (e.g., NDVI)
  
• Validate via cross-validation & field checks

ECa Electrical Conductivity

flowchart LR
  EM38[EM38 (0–50 cm)] --> DR[Depth Response]
  Veris[Veris (0–100 cm)] --> DR
  DR --> Cor[Correlations]
  Cor --> Clay[Clay & CEC]
  Cor --> Sec[OM, silt, moisture]

Soil Water Movement & Crop Growth

• Infiltration driven by texture, structure, OM
  
• Capillary vs gravitational flow in vadose zone
  
• Root uptake via soil-root water potential gradients
  
• VRA irrigation optimizes root-zone moisture
  
• Conservation tillage & cover crops boost infiltration
  
• Integrate water & nutrient management for yields & ecosystem

Field Capacity, Wilting Point, Saturation

flowchart LR
  A[Saturation (0 kPa)] --> B[Field Capacity (−33 kPa)]
  B --> C[Wilting Point (−1500 kPa)]

• Saturation: max θ ≈ porosity
  
• Field Capacity: θ at ~−33 kPa after drainage
  
• Wilting Point: θ at ~−1500 kPa, plant-unavailable
  
• Moisture retention curve: θ vs ψ
  
• Irrigation scheduling: maintain θ between FC & WP
  
• Calibrate ECa–ECw–θ models

Root Growth Zone

flowchart LR
  SP[Soil Traits] --> NORM[Normalize]
  NDVI[NDVI Time Series] --> NORM
  NORM --> CL[Clustering]
  CL --> MAP[Root Zone Map]

Evapotranspiration Definition

• ET = Evaporation + Transpiration flux to atmosphere
  
• Drivers: net radiation, VPD, aerodynamic & canopy conductance
  
• Units: mm·day⁻¹ or latent heat flux (W·m⁻²)
  
• ETc = Kc · ETo (crop vs reference ET)
  
• Potential vs actual ET (moisture-limited)
  
• FAO-56 Penman–Monteith equation

Groundwater Movement Models

flowchart LR
  A[EM Survey] --> B[Regression Calibration]
  B --> C[Volumetric Water]
  C --> D[3D Moisture Surfaces]
  D --> E[Groundwater Model]
  E --> F[Yield Prediction]

• Richards’ equation for unsaturated flow
  
• Hydraulic parameterization via pedotransfer functions
  
• Calibrate with dual-EM vs VWC (4 depths)
  
• Generate 3D moisture & constraint surfaces
  
• GIS integration for boundary conditions
  
• Applications: water-use, risk management, yield analytics

MODFLOW Model Applications

flowchart LR
  A[Piezometers & Probes] --> B[Data Assimilation]
  C[Remote Sensing] --> B
  B --> D[MODFLOW Model]
  D --> E[Hydraulic Heads & Fluxes]
  E --> F[Crop Uptake Coupling]
  F --> G[Irrigation & Nutrient Decisions]

• MODFLOW for 3D groundwater simulations
  
• Data assimilation from piezometers, moisture probes, remote sensing
  
• Inverse modeling calibrates conductivity & recharge
  
• Coupled water uptake & solute transport models
  
• Site-specific irrigation schedules from water-table dynamics
  
• Case: maize irrigation efficiency improvement

Groundwater Pollution & Quality

• Extend 7Rs to protect aquifers from N & P leaching
  
• Geospatial mappings & VRI to match inputs to crop demand
  
• Grassed waterways, riparian buffers, wetlands intercept nutrients
  
• Controlled drainage to limit preferential flow losses
  
• Watershed-scale nutrient flow models for BMP optimization
  
• Adaptive governance under water scarcity & climate change

Soil & Water Conservation Practices

• Contour farming to reduce slope-driven runoff & erosion
  
• Grassed waterways filter sediments & nutrients
  
• Riparian buffers intercept flux & stabilize banks
  
• Sediment ponds & wetlands sequester N & P
  
• VRI applies water by moisture & ET data to minimize leaching
  
• Integrate geospatial mapping with the 7Rs for precision conservation

Contour Farming & Terracing

::: {.columns}

Cover Cropping

• Multispecies cover crops by soil type & climate
  
• UAV & satellite imagery for vigor assessment
  
• Improves structure, OM, moisture retention
  
• N-fixation, P mobilization, reduced leaching
  
• Weed suppression with spot spraying
  
• Data-driven seeding rate & mix deployment

Drainage Strategies & Salinity

• Variable-rate subsurface drainage control
  
• EM & moisture sensors for salinity mapping
  
• Precision grassed waterways & contour drains
  
• Controlled drainage & recycling systems
  
• Watershed-scale salinity flux modeling
  
• Remote sensing for saline hotspot monitoring

Surface Energy Balance Algorithm (SEBAL)

• Estimates ET via surface energy balance
  
• Derives Rn, G, H from satellite data
  
• LE = Rn – G – H for latent heat flux
  
• Uses albedo, NDVI, LST from multispectral imagery
  
• Incorporates T, RH, wind for flux partitioning
  
• High-res ET maps for irrigation & drought

SEBAL Applications

• Field-scale ET for precision irrigation scheduling
  
• Crop water productivity & yield forecasting
  
• Regional water allocation & watershed management
  
• Drought & stress detection across heterogeneous fields
  
• Calibrate hydrological models with SEBAL fluxes
  
• GIS integration for decision support

Practical: Create Soil Map

• Collect georeferenced soil samples on a systematic grid
  
• Analyze pH, OM, texture in lab or via proximal sensors
  
• Interpolate with kriging in geostatistical software
  
• Generate continuous raster layers of soil attributes
  
• Cluster or threshold to define management zones
  
• Export zones as shapefiles/GeoTIFFs for VRA systems

Practical: Management Plan

flowchart LR
  A[Profit-Driver Analysis] --> B[Field Data Collection]
  B --> C[Equipment Calibration]
  C --> D[Generate VRA Maps]
  D --> E[Implementation Schedule]
  E --> F[ROI Monitoring]

Practical: Apply Drainage Strategy

• Multi-layer EM surveys to 1.6 m depth
  
• Calibrate EM vs volumetric water (cores & neutron probes)
  
• Interpolate 3D surfaces of water-holding capacity & constraints
  
• Apply pedotransfer functions in dry zones
  
• Install neutron tubes in high/mean/low EM zones
  
• Integrate datasets for drainage recommendations & yield forecasts

Key Takeaways

• Integrated sensor networks for mass-flow & loss monitoring
  
• Systematic protocols aligned with GRDC industry benchmarks (1–2% loss)
  
• Manufacturer-recommended machine settings optimized per crop & moisture
  
• Grain loss reduction from >2.5% to 1–2% → $37,460 profit gain on 1,000 ha barley
  
• Throughput ↑ and power ↓ via targeted header & cleaning setups
  
• Continuous data-driven calibration ensures sustained efficiency

Next Steps

• Finalize farm-specific profit driver assessment
  
• Map PA profit pathways for yield & input optimization
  
• Apply five diagnostic questions across rainfall zones
  
• Validate feasibility, data needs, and ROI timelines
  
• Review PA technical sources (trials, sensor specs, models)
  
• Consult PA glossary & advisors for implementation planning

Questions & Answers

• Site-specific nutrient management cycles
  
• 4R stewardship in sub-humid vs semi-arid systems
  
• GIS integration & interpolation (IDW, kriging, cokriging)
  
• GNSS-guided VRA strategies
  
• Environmental impact metrics & mitigation
  
• Emerging research: ML recommendations, UAV hyperspectral, automated tools